# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ModelRunner runs the forward passes of the models."""

from __future__ import annotations

import contextlib
import inspect
import logging
import time
from dataclasses import dataclass
from typing import Optional, Union

import torch
import torch.distributed as dist

from sglang.srt.configs.load_config import LoadConfig
from sglang.srt.configs.model_config import (
    AttentionArch,
    ModelConfig,
    ModelImpl,
)
from sglang.srt.configs.update_config import adjust_config_with_unaligned_cpu_tp
from sglang.srt.debug_utils.dumper import dumper
from sglang.srt.distributed import bootstrap
from sglang.srt.distributed.device_communicators.mooncake_transfer_engine import (
    maybe_init_shared_mooncake_transfer_engine,
)
from sglang.srt.distributed.parallel_state_wrapper import ParallelState
from sglang.srt.dllm.config import DllmConfig
from sglang.srt.elastic_ep.elastic_ep import (
    ElasticEPStateManager,
    get_healthy_expert_location_src_rank,
    get_scale_cohort_target,
    join_process_groups,
    join_scale_process_group,
    maybe_rebalance_after_rank_fault,
    maybe_recover_ep_ranks,
    register_scale_cohort,
    try_admit_scale_ranks,
)
from sglang.srt.elastic_ep.expert_backup_client import ExpertBackupClient
from sglang.srt.environ import envs
from sglang.srt.eplb.eplb_manager import EPLBManager
from sglang.srt.eplb.expert_distribution import (
    ExpertDistributionMetrics,
    ExpertDistributionRecorder,
    get_global_expert_distribution_recorder,
    set_global_expert_distribution_recorder,
)
from sglang.srt.eplb.expert_location import (
    ExpertLocationMetadata,
    append_trivial_expert_slots,
    broadcast_global_expert_location_metadata,
    compute_initial_expert_location_metadata,
    format_expert_location_layout,
    get_global_expert_location_metadata,
    set_global_expert_location_metadata,
)
from sglang.srt.eplb.expert_location_updater import ExpertLocationUpdater
from sglang.srt.kv_canary.api import install_canary
from sglang.srt.kv_canary.runner.canary_manager import context_tuple
from sglang.srt.kv_canary.token_oracle.install import install_token_oracle_from_env
from sglang.srt.layers import deep_gemm_wrapper, model_parallel
from sglang.srt.layers.attention.dsa.utils import is_dsa_enable_prefill_cp
from sglang.srt.layers.cp.utils import (
    get_cp_strategy,
    is_cp_v2_active,
)
from sglang.srt.layers.logits_processor import LogitsProcessorOutput
from sglang.srt.layers.sampler import create_sampler
from sglang.srt.layers.torchao_utils import apply_torchao_config_to_model
from sglang.srt.layers.utils.cp_utils import is_mla_prefill_cp_enabled
from sglang.srt.lora.lora_manager import LoRAManager, init_lora_cuda_graph_moe_buffers
from sglang.srt.lora.lora_registry import LoRARef
from sglang.srt.managers.schedule_batch import sanity_check_mm_pad_shift_value
from sglang.srt.mem_cache import kv_cache_dtype
from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
from sglang.srt.mem_cache.kv_cache_configurator import (
    KVCacheConfigurator,
)
from sglang.srt.mem_cache.memory_pool import HybridReqToTokenPool, ReqToTokenPool
from sglang.srt.model_executor.cuda_graph_config import (
    cuda_graph_fully_disabled,
)
from sglang.srt.model_executor.forward_batch_info import (
    ForwardBatch,
    PPProxyTensors,
)
from sglang.srt.model_executor.forward_context import (
    ForwardContext,
    forward_context,
    has_forward_context,
)
from sglang.srt.model_executor.graph_memory_usage import (
    replace_graph_memory_usage,
    replace_graph_time_usage,
)
from sglang.srt.model_executor.model_runner_components import misc_utils
from sglang.srt.model_executor.model_runner_components.attention_backend_setup import (
    build_attention_backends,
    configure_aux_hidden_state_capture,
    get_attention_backend,
    resolve_attention_backend_strs,
)
from sglang.srt.model_executor.model_runner_components.cuda_graph_setup import (
    capture_cuda_graphs,
    capture_decode_graph,
    capture_prefill_graph,
)
from sglang.srt.model_executor.model_runner_components.kv_pool_runtime import (
    compute_post_capture_kv_resize,
    is_post_capture_kv_active,
)
from sglang.srt.model_executor.model_runner_components.layer_setup import (
    ModelLayerInfo,
    adjust_hybrid_swa_layer_ids,
    resolve_layer_indices,
)
from sglang.srt.model_executor.model_runner_components.load_model_utils import (
    build_load_config,
    dist_barrier_after_load,
    load_kv_cache_scales,
    load_model_with_memory_saver,
    maybe_downgrade_dtype_for_legacy_gpu,
    maybe_enable_ipc_weight_cache,
    maybe_precompile_model_kernels_after_loading,
    maybe_register_debug_tensor_dump_hook,
    maybe_trigger_remote_instance_nccl_send_group,
    report_online_quantization,
    resolve_sliding_window_size,
)
from sglang.srt.model_executor.model_runner_components.moe_ep_setup import (
    check_quantized_moe_compatibility,
    init_lplb_solvers,
    prepare_moe_topk,
)
from sglang.srt.model_executor.model_runner_components.ngram_embedding_manager import (
    NgramEmbeddingManager,
)
from sglang.srt.model_executor.model_runner_components.remote_instance_weight_transporter import (
    RemoteInstanceWeightTransporter,
)
from sglang.srt.model_executor.model_runner_components.spec_aux_hidden_state import (
    SpecAuxHiddenStateConfig,
    resolve_spec_aux_hidden_state_config,
)
from sglang.srt.model_executor.model_runner_components.weight_exporter import (
    WeightExporter,
)
from sglang.srt.model_executor.model_runner_components.weight_updater import (
    WeightUpdater,
)
from sglang.srt.model_executor.pool_configurator import MemoryPoolConfig
from sglang.srt.model_executor.runner import (
    EagerRunner,
    get_batch_sizes_to_capture,
)
from sglang.srt.model_executor.runner_utils import make_war_read_done_event
from sglang.srt.platforms import current_platform
from sglang.srt.runtime_context import (
    get_context,
    get_exec,
    get_global_dwdp_manager,
    get_lora,
    get_model,
    get_parallel,
    get_schedule,
    get_spec,
    set_global_dwdp_manager,
)
from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
from sglang.srt.server_args import (  # noqa: F401  (re-export)
    CHUNKED_PREFIX_CACHE_SUPPORTED_ATTENTION_BACKENDS,
    ServerArgs,
    add_chunked_prefix_cache_attention_backend,
    get_global_server_args,
    set_global_server_args_for_scheduler,
)
from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
from sglang.srt.speculative.spec_utils import resolve_num_tokens_per_req
from sglang.srt.state_capturer.base import TopkCaptureOutput
from sglang.srt.state_capturer.indexer_topk import (
    create_indexer_capturer,
    get_global_indexer_capturer,
    set_global_indexer_capturer,
)
from sglang.srt.state_capturer.routed_experts import (
    RoutedExpertsCapturer,
    disable_routed_experts_capture_for_draft,
    get_global_experts_capturer,
    set_global_experts_capturer,
)
from sglang.srt.utils import (
    cpu_has_amx_support,
    enable_show_time_cost,
    get_available_gpu_memory,
    is_host_cpu_arm64,
    is_npu,
    numa_utils,
    require_gathered_buffer,
    reserve_rope_cache_for_long_sequences,
    set_cuda_arch,
    slow_rank_detector,
)
from sglang.srt.utils.device_timer import device_timer_ctx
from sglang.srt.utils.nvtx_pytorch_hooks import PytHooks
from sglang.srt.utils.nvtx_utils import profile_range
from sglang.srt.utils.offloader import (
    create_offloader_from_server_args,
    get_offloader,
    set_offloader,
)
from sglang.srt.utils.profile_utils import build_step_span_name
from sglang.srt.utils.torch_memory_saver_adapter import TorchMemorySaverAdapter
from sglang.srt.utils.weight_checker import WeightChecker

_is_npu = is_npu()
_is_cpu_amx_available = cpu_has_amx_support()
_is_cpu_arm64 = is_host_cpu_arm64()

if _is_npu:
    from sglang.srt.hardware_backend.npu.utils import init_npu_backend

    init_npu_backend()
elif current_platform.is_out_of_tree():
    current_platform.init_backend()

# Detect stragger ranks in model loading


logger = logging.getLogger(__name__)


def _prefill_cuda_graph_allows_context_parallel(
    prefill_runner, forward_batch: ForwardBatch
) -> bool:
    """Allow CP only through a runner that captured the validated CP-v2 body."""
    return get_cp_strategy() is None or (
        bool(getattr(prefill_runner, "enable_cp_v2_bcg_capture", False))
        and is_cp_v2_active(forward_batch)
    )


@dataclass
class ModelRunnerOutput:
    logits_output: Union[LogitsProcessorOutput, PPProxyTensors]
    can_run_graph: bool
    expert_distribution_metrics: Optional[ExpertDistributionMetrics] = None
    routed_experts_output: Optional[TopkCaptureOutput] = None
    indexer_topk_output: Optional[TopkCaptureOutput] = None


def resolve_draft_attention_backend(
    *,
    draft_attention_backend: Optional[str],
    server_args: ServerArgs,
    is_draft_worker: bool,
) -> Optional[str]:
    """The attention backend a runner uses because it is a draft runner.

    ``None`` for a target runner. For a draft: the backend the algorithm that
    built it resolved (the supported-backend fallback in
    ``build_draft_tp_worker``), else ``--speculative-draft-attention-backend``.
    It belongs to the runner, not the process: target and draft coexist.
    """
    if not is_draft_worker:
        return None
    return draft_attention_backend or server_args.speculative_draft_attention_backend


class ModelRunner:
    """ModelRunner runs the forward passes of the models."""

    def __init__(
        self,
        model_config: ModelConfig,
        mem_fraction_static: float,
        gpu_id: int,
        ps: ParallelState,
        nccl_port: int,
        server_args: ServerArgs,
        is_draft_worker: bool = False,
        req_to_token_pool: Optional[ReqToTokenPool] = None,
        token_to_kv_pool_allocator: Optional[BaseTokenToKVPoolAllocator] = None,
        memory_pool_config: Optional[MemoryPoolConfig] = None,
        draft_model_idx: Optional[int] = None,
        draft_attention_backend: Optional[str] = None,
    ):
        # Parse args
        self.mem_fraction_static = mem_fraction_static
        # Set on target by `_resolve_memory_pool_config`; passed in for draft
        # workers so they reuse target's resolved sizes (replaces legacy
        # `server_args._draft_pool_config` mutation hack).
        self.memory_pool_config = memory_pool_config
        self.device = server_args.device
        self.gpu_id = gpu_id
        self.ps = ps
        self.model_config = model_config
        self.dist_port = nccl_port
        self.server_args = server_args
        self.is_draft_worker = is_draft_worker
        self.draft_attention_backend = resolve_draft_attention_backend(
            draft_attention_backend=draft_attention_backend,
            server_args=server_args,
            is_draft_worker=is_draft_worker,
        )
        # This runner's own load format, resolved before anything keys off it:
        # the remote-instance transfer engine is initialized at the top of
        # initialize(), long before the weights are loaded.
        self.draft_load_format = self._resolve_draft_load_format()
        self.is_generation = model_config.is_generation
        self.device_timer = None
        self.is_multimodal = model_config.is_multimodal
        self.is_multimodal_chunked_prefill_supported = (
            model_config.is_multimodal_chunked_prefill_supported
        )
        self.spec_algorithm = SpeculativeAlgorithm.from_string(
            server_args.speculative_algorithm
        )
        self.capture_tail_hooks = []
        self.page_size = server_args.page_size
        self.req_to_token_pool = req_to_token_pool
        self.token_to_kv_pool_allocator = token_to_kv_pool_allocator
        self.mtp_draft_device_pools = ()
        self.is_hybrid_swa = model_config.is_hybrid_swa
        self.is_hybrid_swa_compress = model_config.is_hybrid_swa_compress
        self.use_mla_backend = self.model_config.attention_arch == AttentionArch.MLA
        self.attention_chunk_size = model_config.attention_chunk_size
        self.enable_elastic_ep = server_args.elastic_ep_backend is not None
        self.forward_pass_id = 0
        self._pending_elastic_scale_update = None
        self.init_new_workspace = False
        self.draft_model_idx = draft_model_idx
        self.enable_hisparse = server_args.enable_hisparse

        self.init_startup_observability()

        self.init_remote_instance_weight_transporter()

        self.init_msprobe()

        # auxiliary hidden capture mode. TODO: expose this to server args?
        self.init_spec_aux_hidden_state()

        # Apply the rank zero filter to logger
        if server_args.show_time_cost:
            enable_show_time_cost()

        # Set the global server_args in the scheduler process (target worker
        # only, so a draft init cannot clobber target-derived global state).
        if not self.is_draft_worker:
            set_global_server_args_for_scheduler(server_args)

        misc_utils.maybe_disable_chunked_prefix_cache(
            use_mla_backend=self.use_mla_backend,
            is_draft_worker=self.is_draft_worker,
        )

        # Init OpenMP threads binding for CPU
        if self.device == "cpu":
            self.init_threads_binding()

        # Set float32 matmul precision
        if get_exec().features.enable_tf32_matmul:
            torch.set_float32_matmul_precision("high")

        # Set device early so that TransferEngine init (e.g. Ascend NPU)
        # can access the device context.
        try:
            torch.get_device_module(self.device).set_device(ps.gpu_id)
        except Exception:
            import os

            logger.warning(
                f"Context: {self.device=} {ps.gpu_id=} {os.environ.get('CUDA_VISIBLE_DEVICES')=} {ps.tp_rank=} {ps.tp_size=}"
            )
            raise

        # Initialize MooncakeTransferEngine BEFORE init_torch_distributed so
        # that the shared TE can be passed to the Mooncake PG backend (avoids
        # creating duplicate TransferEngines).
        self.init_shared_mooncake_transfer_engine()

        # Get available memory before model loading.
        # Stored for later use by alloc_memory_pool().
        self.init_torch_distributed()

        self.dcp_size = get_parallel().attn_dcp_size
        self.dcp_rank = get_parallel().attn_dcp_rank

        # Init forward stream for overlap schedule
        self.forward_stream = torch.get_device_module(self.device).Stream()

        # WAR fast-path: a decode-graph forward publishes a fresh event here after
        # load_batch; the scheduler's WAR barrier waits on it (then clears it)
        # instead of the whole-forward wait_stream. None -> whole-forward fallback.
        self.war_fastpath_read_done_event: Optional[torch.cuda.Event] = None
        # Graph runners record this persistent event after shared-state reads.
        self.war_read_done_event = make_war_read_done_event(
            torch.get_device_module(self.device)
        )

        # CPU offload
        set_offloader(
            create_offloader_from_server_args(server_args, dp_rank=self.ps.dp_rank)
        )

        self._weight_checker = WeightChecker(get_model=lambda: self.model, ps=self.ps)

        if envs.SGLANG_DETECT_SLOW_RANK.get():
            slow_rank_detector.execute()

        # Init mindspore running environment when model impl is "mindspore"
        self.init_mindspore_runner()

        # Update deep gemm configure
        if deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM:
            deep_gemm_wrapper.update_deep_gemm_config(gpu_id, server_args)

        # For hisparse (must be set before initialize() so CUDA graph capture can see it)
        self.hisparse_coordinator = None

        # Load model weights and configure
        self.initialize()
        self.check_quantized_moe_compatibility()

        self._initialize_elastic_ep_joiner()

        if self.is_multimodal:
            sanity_check_mm_pad_shift_value(self.model_config.vocab_size)

        # Temporary cached values
        self.support_pp = (
            "pp_proxy_tensors" in inspect.signature(self.model.forward).parameters
        )

        if self.ps.pp_size > 1:
            assert (
                self.support_pp
            ), "Pipeline Parallel is not compatible with this model."

        # For weight updates
        self.init_weight_updater()
        self.init_weight_exporter()

    def init_startup_observability(self) -> None:
        self.weight_load_time = 0.0
        self.graph_memory_usage: dict[str, float] = {}
        self.graph_time_usage: dict[str, float] = {}

    def _initialize_elastic_ep_joiner(self) -> None:
        if not (
            get_exec().moe.elastic_ep_backend is not None
            and self.server_args.is_ep_scale_joiner
        ):
            return

        join_effective_ep_size = get_parallel().ep_join_rank_offset + self.ps.tp_size
        dist.barrier(group=self.tp_group.cpu_group)
        if self.ps.tp_rank == 0:
            register_scale_cohort(
                get_parallel().ep_join_rank_offset,
                join_effective_ep_size,
            )
        join_scale_process_group()
        get_context().override("elastic_ep.scale_join", ep_size=join_effective_ep_size)

        global_ep_rank = self.ps.tp_rank + get_parallel().ep_join_rank_offset
        broadcast_global_expert_location_metadata(
            model_config=self.model_config,
            moe_ep_rank=global_ep_rank,
            src_rank=0,
        )
        set_global_expert_distribution_recorder(
            ExpertDistributionRecorder.init_new(
                self.server_args,
                get_global_expert_location_metadata(),
                rank=global_ep_rank,
            )
        )

        from sglang.srt.layers.dp_attention import (
            enable_joiner_all_gather,
            update_dp_attention_post_scale,
        )

        enable_joiner_all_gather()
        update_dp_attention_post_scale(
            new_dp_size=join_effective_ep_size,
            new_dp_rank=global_ep_rank,
        )
        get_context().override("elastic_ep.scale_join", dp_size=join_effective_ep_size)
        if self.eplb_manager is not None:
            self.eplb_manager.disable_rebalance(
                "EPLB rebalance is disabled while elastic EP scale-up "
                "is being finalized"
            )

        state = ElasticEPStateManager.instance()
        if state is not None:
            state.active_ranks.zero_()
            state.active_ranks[:join_effective_ep_size] = 1
            state.snapshot_active_to_last()
            state.sync_active_to_cpu()
            state.scale_phase = "syncing_new_world"
        self._elastic_scale_ready_barrier(
            target_size=join_effective_ep_size,
            log_tag="JOINER",
        )
        if state is not None:
            state.scale_phase = "serving_expanded"
        self._rearm_eplb_after_elastic_scale()

    def init_msprobe(self):
        self.msprobe_debugger = misc_utils.create_msprobe_debugger(self.server_args)

    def init_weight_updater(self):
        self.weight_updater = WeightUpdater(
            tp_rank=self.ps.tp_rank,
            device=self.device,
            gpu_id=self.gpu_id,
            model_config=self.model_config,
            custom_weight_loaders=get_model().custom_weight_loader,
            get_model=lambda: self.model,
            update_model_fields=self.update_model_fields,
            recapture_cuda_graph=self.init_decode_cuda_graph,
            get_model_runner=lambda: self,
        )

    def init_spec_aux_hidden_state(self):
        self.spec_aux_config: SpecAuxHiddenStateConfig = (
            resolve_spec_aux_hidden_state_config(
                server_args=self.server_args,
                model_config=self.model_config,
                spec_algorithm=self.spec_algorithm,
                is_draft_worker=self.is_draft_worker,
            )
        )

    def init_weight_exporter(self):
        self.weight_exporter = WeightExporter(
            tp_rank=self.ps.tp_rank,
            tp_size=self.ps.tp_size,
            gpu_id=self.gpu_id,
            get_model_path=lambda: self.model_config.model_path,
            get_model=lambda: self.model,
        )

    def init_remote_instance_weight_transporter(self):
        self.remote_instance_weight_transporter = RemoteInstanceWeightTransporter(
            server_args=self.server_args,
            get_model=lambda: self.model,
            tp_rank=self.ps.tp_rank,
            gpu_id=self.gpu_id,
        )

    def init_ngram_embedding_manager(self):
        self.ngram_embedding_manager = NgramEmbeddingManager.from_model(
            model=self.model,
            model_config=self.model_config,
            req_to_token_pool=self.req_to_token_pool,
            server_args=self.server_args,
            max_running_requests=self.max_running_requests,
            device=self.device,
        )

    def init_kv_cache_configurator(self):
        self.kv_cache_configurator = KVCacheConfigurator(
            device=self.device,
            gpu_id=self.gpu_id,
            ps=self.ps,
            pp_group=self.pp_group,
            model=self.model,
            model_config=self.model_config,
            server_args=self.server_args,
            kv_cache_dtype=self.kv_cache_dtype,
            kv_cache_dtype_str=self.kv_cache_dtype_str,
            model_dtype=self.dtype,
            page_size=self.page_size,
            sliding_window_size=self.sliding_window_size,
            spec_algorithm=self.spec_algorithm,
            is_draft_worker=self.is_draft_worker,
            post_capture_kv_active=is_post_capture_kv_active(
                server_args=self.server_args, is_draft_worker=self.is_draft_worker
            ),
            spec_aux_config=self.spec_aux_config,
            is_hybrid_swa=self.is_hybrid_swa,
            is_hybrid_swa_compress=self.is_hybrid_swa_compress,
            use_mla_backend=self.use_mla_backend,
            layer_info=self.layer_info,
            forward_stream=self.forward_stream,
            req_to_token_pool=self.req_to_token_pool,
            token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
            memory_pool_config=self.memory_pool_config,
            draft_model_idx=self.draft_model_idx,
        )

    def init_mindspore_runner(self):
        # Init the mindspore runner
        # for now, there is only some communication initialization work
        if get_model().model_impl.lower() == ModelImpl.MINDSPORE and _is_npu:
            from sglang.srt.model_executor.mindspore_runner import init_ms_distributed

            init_ms_distributed(
                world_size=self.ps.tp_size * self.ps.pp_size,
                rank=self.ps.tp_size * self.ps.pp_rank + self.ps.tp_rank,
                local_rank=self.gpu_id,
                server_args=self.server_args,
                port=self.dist_port,
            )

    def initialize(self):
        self.init_memory_saver_adapter()
        self.maybe_init_remote_instance_transfer_engine()
        self.maybe_init_expert_location_metadata()
        self.maybe_init_lplb_solvers()
        self.maybe_init_eplb_manager()
        self.expert_location_updater = ExpertLocationUpdater()
        self.maybe_init_elastic_ep()
        self.init_token_oracle()
        self.sampler = create_sampler()
        self.load_model()
        prepare_moe_topk(
            model=self.model,
            model_config=self.model_config,
            server_args=self.server_args,
            moe_ep_size=self.ps.moe_ep_size,
            moe_ep_rank=self.ps.moe_ep_rank,
        )

        self.maybe_init_dwdp()

        # Must run before backend/graph init so no draft graph records a
        # routed-experts capture-write kernel.
        if self.is_draft_worker:
            disable_routed_experts_capture_for_draft(self.model)
        self.maybe_init_expert_backup_client()
        self.remote_instance_weight_transporter.maybe_register_and_publish_weight_info()
        self.layer_info: ModelLayerInfo = resolve_layer_indices(
            model=self.model,
            model_config=self.model_config,
            is_draft_worker=self.is_draft_worker,
            spec_algorithm=self.spec_algorithm,
        )
        adjust_hybrid_swa_layer_ids(
            model_config=self.model_config,
            start_layer=self.layer_info.start_layer,
            end_layer=self.layer_info.end_layer,
            is_hybrid_swa=self.is_hybrid_swa,
        )
        self.maybe_apply_post_load_model_transforms()
        self.maybe_init_lora_manager()
        self.maybe_enable_batch_invariant_mode()
        self.configure_kv_cache_dtype()

    def init_memory_saver_adapter(self):
        self.memory_saver_adapter = TorchMemorySaverAdapter.create(
            enable=get_exec().features.enable_memory_saver
        )

    def maybe_init_remote_instance_transfer_engine(self):
        if self.server_args.remote_instance_weight_loader_use_transfer_engine(
            load_format=self.draft_load_format
        ):
            self.remote_instance_weight_transporter.init_engine()

    def maybe_init_expert_location_metadata(self):
        if self.is_draft_worker:
            return
        expert_rank = self.ps.moe_ep_rank + (
            get_parallel().ep_join_rank_offset
            if self.server_args.is_ep_scale_joiner
            else 0
        )
        set_global_expert_location_metadata(
            compute_initial_expert_location_metadata(
                server_args=self.server_args,
                model_config=self.model_config,
                moe_ep_rank=expert_rank,
            )
        )
        if self.ps.tp_rank == 0 and envs.SGLANG_LOG_EXPERT_LOCATION_METADATA.get():
            logger.info(
                "Initial expert_location_metadata:\n%s",
                format_expert_location_layout(get_global_expert_location_metadata()),
            )
        set_global_expert_distribution_recorder(
            ExpertDistributionRecorder.init_new(
                self.server_args,
                get_global_expert_location_metadata(),
                rank=expert_rank,
            )
        )

    def maybe_init_lplb_solvers(self):
        if get_exec().moe.ep_dispatch_algorithm == "lp" and not self.is_draft_worker:
            init_lplb_solvers(model_config=self.model_config)

    def maybe_init_eplb_manager(self):
        self.eplb_manager = (
            EPLBManager(
                server_args=self.server_args,
                model_config=self.model_config,
                ps=self.ps,
                get_model=lambda: self.model,
                get_expert_location_updater=lambda: self.expert_location_updater,
                get_expert_backup_client=lambda: self.expert_backup_client,
                get_weight_updater=lambda: self.weight_updater,
            )
            if get_exec().moe.enable_eplb and (not self.is_draft_worker)
            else None
        )

    def maybe_init_elastic_ep(self):
        if get_exec().moe.elastic_ep_backend:
            ElasticEPStateManager.init(self.server_args)

    def init_token_oracle(self):
        self._token_oracle_manager = install_token_oracle_from_env(
            server_args=self.server_args,
            vocab_size=self.model_config.vocab_size,
        )

    def maybe_init_expert_backup_client(self):
        self.expert_backup_client = (
            ExpertBackupClient(
                server_args=self.server_args,
                model_config=self.model_config,
                moe_ep_size=self.ps.moe_ep_size,
                moe_ep_rank=self.ps.moe_ep_rank,
                get_model=lambda: self.model,
            )
            if (
                get_exec().moe.enable_elastic_expert_backup
                and get_exec().moe.elastic_ep_backend is not None
            )
            else None
        )

    def maybe_apply_post_load_model_transforms(self):
        # In layered loading, torchao may have been applied
        torchao_applied = getattr(self.model, "torchao_applied", False)
        if not torchao_applied:
            apply_torchao_config_to_model(self.model, get_exec().graph.torchao_config)
        supports_torch_tp = getattr(self.model, "supports_torch_tp", False)
        if self.ps.tp_size > 1 and supports_torch_tp:
            self.apply_torch_tp()

    def maybe_init_lora_manager(self):
        if get_lora().enable_lora:
            self.init_lora_manager()

    def maybe_enable_batch_invariant_mode(self):
        if get_exec().deterministic.enable_deterministic_inference:
            from sglang.srt.batch_invariant_ops import enable_batch_invariant_mode

            enable_batch_invariant_mode()

    def get_pp_proxy_topk_size(self) -> Optional[int]:
        return misc_utils.resolve_pp_proxy_topk_size(
            model_config=self.model_config,
            pp_size=self.ps.pp_size,
            pp_rank=self.ps.pp_rank,
            start_layer=self.layer_info.start_layer,
        )

    def get_pp_proxy_residual_num_blocks(self) -> Optional[int]:
        return misc_utils.resolve_pp_proxy_residual_num_blocks(
            model_config=self.model_config,
            pp_size=self.ps.pp_size,
            pp_rank=self.ps.pp_rank,
            start_layer=self.layer_info.start_layer,
        )

    def decode_num_tokens_per_req(
        self, *, num_draft_tokens: Optional[int] = None
    ) -> int:
        """Logits rows per decode batch slot."""
        if self.spec_algorithm.is_speculative():
            return resolve_num_tokens_per_req(
                phase="target_verify",
                server_args=self.server_args,
                spec_algorithm=self.spec_algorithm,
                is_draft_worker=self.is_draft_worker,
                num_draft_tokens=num_draft_tokens,
            )
        dllm_config = DllmConfig.from_server_args(self.server_args)
        return dllm_config.block_size if dllm_config is not None else 1

    def max_decode_logits_rows(self) -> int:
        """Rows the shared logits buffer needs."""
        num_tokens_per_req = self.decode_num_tokens_per_req()
        capture_bs, _ = get_batch_sizes_to_capture(self, num_tokens_per_req)
        return max(capture_bs) * num_tokens_per_req

    def alloc_memory_pool(self, memory_pool_config: Optional[MemoryPoolConfig] = None):
        """Allocate KV cache memory pools only (no backends or cuda graphs)."""
        if memory_pool_config is not None:
            self.memory_pool_config = memory_pool_config

        self.init_kv_cache_configurator()
        result = self.kv_cache_configurator.configure(
            pre_model_load_memory=self.pre_model_load_memory
        )
        self.max_total_num_tokens = result.max_total_num_tokens
        self.max_running_requests = result.max_running_requests
        self.req_to_token_pool = result.req_to_token_pool
        self.token_to_kv_pool = result.token_to_kv_pool
        self.token_to_kv_pool_allocator = result.token_to_kv_pool_allocator
        self.memory_pool_config = result.memory_pool_config
        if self.is_hybrid_swa:
            self.full_max_total_num_tokens = result.full_max_total_num_tokens
            self.swa_max_total_num_tokens = result.swa_max_total_num_tokens
        # Keep a reference so the shared byte buffer is not GC'd.
        self._unified_memory_pool = result.unified_memory_pool

        self._init_post_memory_pool_components()

    def _init_post_memory_pool_components(self):
        """Post-pool component wiring, split out of alloc_memory_pool so forks
        that build bespoke memory pools can reuse it after allocating them."""
        # Must be called AFTER init_memory_pool so the pool object exists for
        # canary to monkey-patch, and BEFORE init_decode_cuda_graph so warmup
        # forwards captured into the graph see the patched pool methods.
        self.canary_manager = install_canary(
            server_args=self.server_args,
            model_runner=self,
            token_oracle_manager=self._token_oracle_manager,
        )

        # Init ngram embedding token table
        self.init_ngram_embedding_manager()

        self.maybe_init_hisparse_coordinator()

        self.init_routed_experts_capturer()
        self.init_indexer_capturer()

        self.graph_shared_output = None

    def maybe_init_hisparse_coordinator(self):
        if not self.enable_hisparse:
            return
        from sglang.srt.managers.hisparse_coordinator import HiSparseCoordinator
        from sglang.srt.mem_cache.sparsity import parse_hisparse_config

        hisparse_cfg = parse_hisparse_config(self.server_args)
        hisparse_top_k = getattr(
            self.model_config.hf_text_config, "index_topk", hisparse_cfg.top_k
        )
        self.hisparse_coordinator = HiSparseCoordinator(
            req_to_token_pool=self.req_to_token_pool,
            token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
            top_k=hisparse_top_k,
            device_buffer_size=hisparse_cfg.device_buffer_size,
            device=self.device,
            tp_group=(
                self.attention_tp_group.cpu_group
                if get_parallel().enable_dp_attention
                else self.tp_group.cpu_group
            ),
            host_to_device_ratio=hisparse_cfg.host_to_device_ratio,
            swap_in_block_size=hisparse_cfg.swap_in_block_size,
        )

    def post_capture_resize_kv_pool(self):
        resize = compute_post_capture_kv_resize(self)
        self.max_total_num_tokens = resize.max_total_num_tokens
        if self.is_hybrid_swa:
            self.full_max_total_num_tokens = resize.full_max_total_num_tokens
            self.swa_max_total_num_tokens = resize.swa_max_total_num_tokens
        if self.memory_pool_config is not None:
            self.memory_pool_config.max_total_num_tokens = resize.max_total_num_tokens
            self.memory_pool_config.full_max_total_num_tokens = (
                resize.full_max_total_num_tokens
            )
            self.memory_pool_config.swa_max_total_num_tokens = (
                resize.swa_max_total_num_tokens
            )
        if resize.capped_max_running_requests is not None:
            self.max_running_requests = resize.capped_max_running_requests
            if self.memory_pool_config is not None:
                self.memory_pool_config.max_running_requests = (
                    resize.capped_max_running_requests
                )

    def post_capture_elastic_ep_recover(self):
        join_process_groups()

        global_ep_rank = self.ps.tp_rank + get_parallel().ep_join_rank_offset
        broadcast_global_expert_location_metadata(
            model_config=self.model_config,
            moe_ep_rank=global_ep_rank,
            src_rank=get_healthy_expert_location_src_rank(
                invoked_in_elastic_ep_rejoin_path=True
            ),
        )
        set_global_expert_distribution_recorder(
            ExpertDistributionRecorder.init_new(
                self.server_args,
                get_global_expert_location_metadata(),
                rank=global_ep_rank,
            )
        )

        ElasticEPStateManager.instance().reset()

    def init_attention_backends(self):
        """Initialize attention backends only (no cuda graph capture)."""
        # Must be called BEFORE init_decode_cuda_graph() so CUDA graph capture
        # runs with aux hidden state capture enabled.
        configure_aux_hidden_state_capture(
            model=self.model,
            eagle_use_aux_hidden_state=self.spec_aux_config.eagle_use_aux_hidden_state,
            eagle_aux_hidden_state_layer_ids=self.spec_aux_config.eagle_aux_hidden_state_layer_ids,
            dflash_use_aux_hidden_state=self.spec_aux_config.dflash_use_aux_hidden_state,
            dflash_target_layer_ids=self.spec_aux_config.dflash_target_layer_ids,
            is_dspark=self.spec_algorithm.is_dspark(),
        )
        # Resolve before building: backends read the pair off the runner while
        # they construct (the FlashInfer KV-access check).
        resolved = resolve_attention_backend_strs(model_runner=self)
        self.prefill_attention_backend_str = resolved.prefill
        self.decode_attention_backend_str = resolved.decode
        backends = build_attention_backends(model_runner=self)
        self.attn_backend = backends.attn_backend
        self.decode_attn_backend = backends.decode_attn_backend
        self.decode_attn_backend_group = backends.decode_attn_backend_group

        if get_parallel().dcp_enabled and get_parallel().dcp_replicate_q_proj:
            self._prepare_replicated_q_proj()

    def _prepare_replicated_q_proj(self) -> None:
        # --dcp-replicate-q-proj: gather each rank's attn_tp head-shard of
        # q_b_proj / w_kc into full-head buffers once here (pre-capture) so the
        # MLA decode path can skip the per-layer Q all-gather. bf16/fp16 only.
        from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
        from sglang.srt.models.deepseek_v2 import DeepseekV2AttentionMLA

        dcp_group = get_parallel().dcp_group
        if dcp_group.world_size <= 1:
            return
        n_prepared = 0
        for m in self.model.modules():
            if not isinstance(m, DeepseekV2AttentionMLA):
                continue
            if m.w_kc is None:
                continue
            qp = m.q_b_proj if m.has_q_b_proj else m.q_proj
            # q-replicate only supports the unquantized bf16/fp16 absorb path;
            # quantized q-proj (packed weights) and non-16-bit w_kc keep the
            # per-layer Q all-gather.
            if (
                m.w_kc.dtype not in (torch.bfloat16, torch.float16)
                or not isinstance(qp.quant_method, UnquantizedLinearMethod)
                or qp.weight.dtype not in (torch.bfloat16, torch.float16)
            ):
                logger.warning(
                    "dcp_replicate_q_proj: skipping quantized q-proj/w_kc "
                    "(bf16/fp16 only); this layer keeps the Q all-gather."
                )
                continue
            m.w_kc_qrep = dcp_group.all_gather(m.w_kc.contiguous(), dim=0)
            m.q_b_proj_qrep_weight = dcp_group.all_gather(
                qp.weight.data.contiguous(), dim=0
            )
            n_prepared += 1
        logger.info(
            "dcp_replicate_q_proj: prepared full-head Q weights for %d MLA layers",
            n_prepared,
        )

    def init_cuda_graphs(self, capture_decode_cuda_graph: bool = True):
        capture = capture_cuda_graphs(
            model_runner=self, capture_decode_cuda_graph=capture_decode_cuda_graph
        )
        self.eager_runner = capture.eager_runner
        self.prefill_cuda_graph_runner = capture.prefill.runner
        self.decode_cuda_graph_runner = capture.decode.runner
        self.graph_memory_usage = capture.memory_usage
        self.graph_time_usage = capture.time_usage

    def init_routed_experts_capturer(self):
        if self.is_draft_worker:
            # Capture is target-only. The draft worker runs in the same process
            # as its target and inits after it, so installing a capturer here
            # would overwrite the target's process-global one.
            return

        set_global_experts_capturer(
            RoutedExpertsCapturer.create(
                model=self.model,
                model_config=self.model_config,
                num_tokens=self.max_total_num_tokens + self.page_size,
                max_running_requests=self.max_running_requests,
                device=self.device,
            )
        )

    def init_indexer_capturer(self):
        set_global_indexer_capturer(
            create_indexer_capturer(
                model_config=self.model_config,
                num_tokens=self.max_total_num_tokens + self.page_size,
                max_running_requests=self.max_running_requests,
                device=self.device,
            )
        )

    def check_quantized_moe_compatibility(self):
        check_quantized_moe_compatibility(
            model_config=self.model_config,
            tp_size=self.ps.tp_size,
            moe_ep_size=self.ps.moe_ep_size,
            moe_dp_size=self.ps.moe_dp_size,
        )

    def init_torch_distributed(self):
        result = bootstrap.init_torch_distributed(
            server_args=self.server_args,
            model_config=self.model_config,
            device=self.device,
            ps=self.ps,
            dist_port=self.dist_port,
            is_draft_worker=self.is_draft_worker,
            local_omp_cpuid=self.local_omp_cpuid if self.device == "cpu" else None,
        )
        self.tp_group = result.tp_group
        self.pp_group = result.pp_group
        self.attention_tp_group = result.attention_tp_group
        self.pre_model_load_memory = result.pre_model_load_memory

    def init_shared_mooncake_transfer_engine(self):
        maybe_init_shared_mooncake_transfer_engine(
            server_args=self.server_args, gpu_id=self.gpu_id
        )

    def load_model(self):
        tic_total = time.perf_counter()
        before_avail_memory = get_available_gpu_memory(self.device, self.gpu_id)
        logger.info(
            f"Load weight begin. avail mem={get_available_gpu_memory(self.device, self.gpu_id):.2f} GB"
        )

        # This can reduce thread conflicts and speed up weight loading.
        if self.device != "cpu":
            torch.set_num_threads(1)
        if self.device == "cuda":
            maybe_downgrade_dtype_for_legacy_gpu(
                server_args=self.server_args, model_config=self.model_config
            )

        set_cuda_arch()

        draft_load_format = self.draft_load_format
        self.load_config = build_load_config(
            server_args=self.server_args,
            load_format=draft_load_format,
            tp_rank=self.ps.tp_rank,
            remote_instance_weight_transporter_engine=self.remote_instance_weight_transporter.engine,
            remote_instance_weight_transporter_session_id=self.remote_instance_weight_transporter.session_id,
            draft_model_idx=self.draft_model_idx,
            weight_cache_mode=get_model().weight_cache_mode,
            weight_cache_socket=get_model().weight_cache_socket,
        )

        # If the weight cache is enabled, override the load format to IPC_CACHE
        # and derive the per-rank daemon socket. Idempotent across reloads.
        maybe_enable_ipc_weight_cache(
            load_config=self.load_config,
            server_args=self.server_args,
            tp_size=self.ps.tp_size,
            pp_rank=self.ps.pp_rank,
            tp_rank=self.ps.tp_rank,
        )
        if self.device == "cpu":
            self.model_config = adjust_config_with_unaligned_cpu_tp(
                self.model_config, self.load_config, self.ps.tp_size
            )

        maybe_trigger_remote_instance_nccl_send_group(
            server_args=self.server_args,
            tp_rank=self.ps.tp_rank,
            load_format=draft_load_format,
        )

        with self._load_format_scope(draft_load_format):
            loaded = load_model_with_memory_saver(
                server_args=self.server_args,
                model_config=self.model_config,
                load_config=self.load_config,
                device=self.device,
                gpu_id=self.gpu_id,
                memory_saver_adapter=self.memory_saver_adapter,
                is_draft_worker=self.is_draft_worker,
            )
        self.loader = loaded.loader
        self.model = loaded.model
        if loaded.remote_instance_weight_info is not None:
            self.remote_instance_weight_transporter.weight_info = (
                loaded.remote_instance_weight_info
            )

        if not self.is_draft_worker:
            get_offloader().post_init()

        self.maybe_precompile_model_kernels_after_loading()

        # Register model for layerwise NVTX profiling if enabled
        if get_exec().comm.enable_layerwise_nvtx_marker:
            pyt_hooks = PytHooks()
            pyt_hooks.register_hooks(self.model, module_prefix="model")

        load_kv_cache_scales(model=self.model, server_args=self.server_args)

        self.sliding_window_size = resolve_sliding_window_size(
            self.model, self.model_config
        )

        self.prefill_aware_swa = (
            hasattr(self.model, "is_prefill_aware_swa")
            and self.model.is_prefill_aware_swa()
        )

        self.dtype = self.model_config.dtype

        after_avail_memory = get_available_gpu_memory(self.device, self.gpu_id)
        self.weight_load_mem_usage = before_avail_memory - after_avail_memory
        self.weight_load_time = time.perf_counter() - tic_total
        # Get quantization config from ModelConfig
        # This handles both config.json (standard) and hf_quant_config.json (ModelOpt)
        quant_str = self.model_config.get_quantization_config_log_str()

        logger.info(
            f"Load weight end. "
            f"elapsed={self.weight_load_time:.2f} s, "
            f"type={type(self.model).__name__}, "
            f"{quant_str + ', ' if quant_str else ''}"
            f"avail mem={after_avail_memory:.2f} GB, "
            f"mem usage={self.weight_load_mem_usage:.2f} GB."
        )

        report_online_quantization(model=self.model, server_args=self.server_args)

        maybe_register_debug_tensor_dump_hook(
            model=self.model,
            server_args=self.server_args,
            spec_algorithm=self.spec_algorithm,
            is_draft_worker=self.is_draft_worker,
            tp_size=self.ps.tp_size,
            tp_rank=self.ps.tp_rank,
            pp_rank=self.ps.pp_rank,
        )

        if dumper.may_enable:
            dumper.apply_source_patches()
            dumper.register_non_intrusive_dumper(self.model)

        # Pre-expand RoPE cache before CUDA Graph capture
        reserve_rope_cache_for_long_sequences(
            self.model,
            self.server_args,
            self.model_config,
            logger,
        )

        dist_barrier_after_load(
            elastic_ep_backend=get_exec().moe.elastic_ep_backend,
            tp_rank=self.ps.tp_rank,
            is_ep_joiner=self.server_args.is_ep_joiner,
        )

    def maybe_precompile_model_kernels_after_loading(self) -> None:
        maybe_precompile_model_kernels_after_loading(self.model, self.device)

    def maybe_init_dwdp(self):
        if self.is_draft_worker:
            return
        if get_parallel().dwdp_size <= 1:
            return
        from sglang.srt.layers.moe.dwdp import DwdpManager

        manager = DwdpManager(self.server_args)
        set_global_dwdp_manager(manager)
        manager.setup(self.model)

    def init_lora_manager(self):
        self.lora_manager = LoRAManager(
            base_model=self.model,
            base_hf_config=self.model_config.hf_config,
            max_loras_per_batch=get_lora().max_loras_per_batch,
            load_config=self.load_config,
            dtype=self.dtype,
            server_args=self.server_args,
            lora_backend=get_lora().lora_backend,
            tp_size=self.ps.tp_size,
            tp_rank=self.ps.tp_rank,
            max_lora_rank=get_lora().max_lora_rank,
            target_modules=get_lora().lora_target_modules,
            lora_paths=get_lora().lora_paths,
        )
        if not cuda_graph_fully_disabled():
            init_lora_cuda_graph_moe_buffers(
                server_args=self.server_args,
                model=self.model,
                lora_manager=self.lora_manager,
                dtype=self.dtype,
            )

    def load_lora_adapter(self, lora_ref: LoRARef):
        """Load a new lora adapter from disk or huggingface."""
        return self.lora_manager.load_lora_adapter(lora_ref)

    def load_lora_adapter_from_tensors(
        self, lora_ref: LoRARef, tensors, config_dict, added_tokens_config=None
    ):
        return self.lora_manager.load_lora_adapter_from_tensors(
            lora_ref, tensors, config_dict, added_tokens_config
        )

    def unload_lora_adapter(self, lora_ref: LoRARef):
        """Unload a lora adapter that was previously loaded during initialization or dynamic loading."""
        return self.lora_manager.unload_lora_adapter(lora_ref)

    @property
    def effective_max_total_num_tokens(self):
        """Return the max token pool size considering hybrid swa settings."""
        if self.is_hybrid_swa:
            return self.full_max_total_num_tokens or self.swa_max_total_num_tokens
        else:
            return self.max_total_num_tokens

    def _load_format_scope(self, load_format: Optional[str]):
        """Make this runner's load format the published one while it loads.

        Model code reads it off the bag during construction (Inkling replaces
        per-element noise in its shared-expert scales under dummy loading), so a
        draft loading a different way than the target needs its own value live
        for the load, and the target's back afterwards.
        """
        if load_format is None:
            return contextlib.nullcontext()
        return get_model().override(load_format=load_format)

    def _resolve_draft_load_format(self) -> Optional[str]:
        """``--speculative-draft-load-format``, for a draft runner only.

        The draft loads its own checkpoint, so its load format is this runner's
        own resolved value; the target keeps ``--load-format``.
        """
        if not self.is_draft_worker:
            return None
        load_format = get_spec().speculative_draft_load_format
        if load_format is not None:
            logger.info(f"Using draft model load_format: '{load_format}'")
        return load_format

    def configure_kv_cache_dtype(self):
        spec_algorithm = getattr(self, "spec_algorithm", None)
        resolved_kv_cache_dtype, self.kv_cache_dtype = (
            kv_cache_dtype.configure_kv_cache_dtype(
                server_args_kv_cache_dtype=self.server_args.kv_cache_dtype,
                model=getattr(self, "model", None),
                model_dtype=getattr(self, "dtype", torch.bfloat16),
                is_draft_worker=getattr(self, "is_draft_worker", False),
                is_dflash=(
                    spec_algorithm.is_dflash_family()
                    if spec_algorithm is not None
                    else False
                ),
                speculative_draft_attention_backend=self.draft_attention_backend,
            )
        )
        # This runner's OWN resolved dtype string (target or draft). Attention
        # backends read it directly instead of the process-global get_model()
        # bag: a draft runner does not publish its args, so the bag would carry
        # the target's dtype and mis-drive the draft's FP8 cast/descale paths.
        self.kv_cache_dtype_str = (
            resolved_kv_cache_dtype
            if resolved_kv_cache_dtype is not None
            else self.server_args.kv_cache_dtype
        )

    def _get_attention_backend(self, init_new_workspace: bool = False):
        return get_attention_backend(
            model_runner=self, init_new_workspace=init_new_workspace
        )

    def _decode_cuda_graph_runner_cls(self):
        """Decode CUDA-graph runner class to construct.

        Subclasses can override this to install specialized decode graph runners.
        """
        from sglang.srt.model_executor.runner.decode_cuda_graph_runner import (
            DecodeCudaGraphRunner,
        )

        return DecodeCudaGraphRunner

    def init_decode_cuda_graph(self):
        self.decode_cuda_graph_runner = None
        capture = capture_decode_graph(model_runner=self)
        self.decode_cuda_graph_runner = capture.runner
        self.graph_memory_usage = replace_graph_memory_usage(
            self.graph_memory_usage,
            capture.memory_usage,
            phases=("decode", "target_verify", "draft_decode"),
        )
        self.graph_time_usage = replace_graph_time_usage(
            self.graph_time_usage,
            capture.time_usage,
            phases=("decode", "target_verify", "draft_decode"),
        )

    def init_prefill_cuda_graph(self, force_for_draft_worker: bool = False):
        self.prefill_cuda_graph_runner = None
        capture = capture_prefill_graph(
            model_runner=self,
            eager_runner=self.eager_runner,
            force_for_draft_worker=force_for_draft_worker,
        )
        self.prefill_cuda_graph_runner = capture.runner
        self.graph_memory_usage = replace_graph_memory_usage(
            self.graph_memory_usage,
            capture.memory_usage,
            phases=("prefill", "draft_prefill"),
        )
        self.graph_time_usage = replace_graph_time_usage(
            self.graph_time_usage,
            capture.time_usage,
            phases=("prefill", "draft_prefill"),
        )

    def init_threads_binding(self):
        self.local_omp_cpuid = numa_utils.init_threads_binding(
            tp_rank=self.ps.tp_rank, tp_size=self.ps.tp_size
        )

    def apply_torch_tp(self):
        model_parallel.apply_torch_tp(
            model=self.model, device=self.device, tp_size=self.ps.tp_size
        )

    def update_decode_attn_backend(self, stream_idx: int):
        self.decode_attn_backend = self.decode_attn_backend_group[stream_idx]

    def prepare_dummy_forward_batch(self, forward_batch: ForwardBatch) -> ForwardBatch:
        """Customize a runner-created dummy batch before attention metadata initialization."""
        return forward_batch

    def _prepare_eager_forward_batch(self, forward_batch: ForwardBatch) -> None:
        """Pad / normalize a batch for the eager (non-cuda-graph) forward.

        Runs the DP/MLP-sync padding, the attn-tp num_token_non_padded
        normalization, and the hisparse-coordinator refresh that the eager
        forward path needs — the cuda-graph path does the equivalent inside the
        runner's capture/replay, so this is skipped there.
        """
        # For MLP sync
        if forward_batch.global_num_tokens_cpu is not None:
            forward_batch.prepare_mlp_sync_batch(self)
        else:
            forward_batch.prepare_attn_tp_scatter_input(self)

        # Normalize num_token_non_padded to be local to this attention TP rank if needed.
        # The skip is scoped to DSACPLayerCommunicator-style CP (DSA, MLA): those
        # flavors already feed a zigzag-split rank-local layout whose token count
        # should not be further divided by attn_tp_size. MHA-arch prefill CP
        # (Qwen3/Qwen2 MoE) keeps the attn_tp-replicated layout and wants the
        # adjustment to run — see docs/design/prefill-cp-mla.md §Phase 5.
        if (
            forward_batch.num_token_non_padded is not None
            and forward_batch.global_num_tokens_gpu is not None
            and require_gathered_buffer(self.server_args)
            and not is_dsa_enable_prefill_cp()
            and not is_mla_prefill_cp_enabled()
        ):
            forward_batch.adjust_num_token_non_padded_for_attn_tp(
                server_args=self.server_args,
            )

        # Hisparse coordinator — backends now read it from self.model_runner.
        if self.hisparse_coordinator is not None:
            self.hisparse_coordinator.num_real_reqs.fill_(forward_batch.batch_size)

    def _pp_kwargs(self, pp_proxy_tensors) -> dict:
        """Build the pp_proxy_tensors forward kwarg, in one place.

        Pipeline-parallel proxy tensors are threaded into model.forward only
        when the model accepts them (``support_pp``).
        """
        return {"pp_proxy_tensors": pp_proxy_tensors} if self.support_pp else {}

    def _extend_forward_kwargs(
        self, forward_batch: ForwardBatch, pp_proxy_tensors
    ) -> dict:
        """Build the extend/prefill model.forward kwargs (pp_proxy_tensors +
        input_embeds / replace_embeds overrides + get_embedding), shared by the
        prefill cuda-graph path and the EagerRunner's eager extend path."""
        kwargs = self._pp_kwargs(pp_proxy_tensors)
        if forward_batch.input_embeds is not None:
            kwargs["input_embeds"] = forward_batch.input_embeds.bfloat16()
        if (
            forward_batch.replace_embeds is not None
            and forward_batch.replace_positions is not None
        ):
            # Token embedding overrides: get base embeddings, scatter replacements
            if "input_embeds" not in kwargs:
                embed_layer = self.model.get_input_embeddings()
                kwargs["input_embeds"] = embed_layer(forward_batch.input_ids)
            kwargs["input_embeds"][forward_batch.replace_positions] = (
                forward_batch.replace_embeds.to(kwargs["input_embeds"].dtype)
            )
        if not self.is_generation:
            kwargs["get_embedding"] = True
        return kwargs

    def forward_split_prefill(
        self,
        forward_batch: ForwardBatch,
        reinit_attn_backend: bool = False,
        forward_count: int = 1,
    ) -> LogitsProcessorOutput:
        if forward_batch.split_index == 0 or reinit_attn_backend:
            self.attn_backend.init_forward_metadata(forward_batch)
        next_split_index = min(
            forward_batch.split_index + forward_count,
            self.model_config.num_hidden_layers,
        )
        with device_timer_ctx(self.device_timer, "split_prefill"):
            ret = self.model.forward_split_prefill(
                forward_batch.input_ids,
                forward_batch.positions,
                forward_batch,
                (forward_batch.split_index, next_split_index),
            )
        forward_batch.split_index = next_split_index
        return ret

    def forward(
        self,
        forward_batch: ForwardBatch,
        skip_attn_backend_init: Optional[bool] = None,  # deprecated
        pp_proxy_tensors: Optional[PPProxyTensors] = None,
        reinit_attn_backend: bool = False,
        split_forward_count: int = 1,
    ) -> ModelRunnerOutput:
        # Deprecated kwarg: pre-planners mark the batch themselves now.
        forward_batch.apply_deprecated_skip_attn_backend_init(skip_attn_backend_init)

        self.forward_pass_id += 1

        # Try msprob debugger
        if self.msprobe_debugger is not None:
            rank_id = (
                self.gpu_id
                if self.ps.attn_dp_size is not None and self.ps.attn_dp_size > 1
                else None
            )
            self.msprobe_debugger.start(model=self.model, rank_id=rank_id)

        # Step span
        step_span_ctx = profile_range(build_step_span_name(forward_batch))

        canary_ctx = (
            context_tuple(
                c.with_ops_outside_graph(
                    single_forward_indices=[0],
                    maybe_inaccurate_forward_batch=forward_batch,
                ),
                c.with_active_single_forward_manager(0),
            )
            if not self.is_draft_worker and ((c := self.canary_manager) is not None)
            else contextlib.nullcontext()
        )

        with (
            canary_ctx,
            step_span_ctx,
            get_global_expert_distribution_recorder().with_forward_pass(
                self.forward_pass_id,
                forward_batch,
            ) as recorder_outputs,
        ):
            output = self._forward_raw(
                forward_batch,
                pp_proxy_tensors,
                reinit_attn_backend,
                split_forward_count,
            )
            if self.enable_elastic_ep:
                output = self._maybe_rebalance_after_rank_fault(
                    output,
                    forward_batch,
                    pp_proxy_tensors,
                    reinit_attn_backend,
                    split_forward_count,
                )
        output.expert_distribution_metrics = recorder_outputs.get("metrics")

        no_copy_to_cpu = not get_schedule().disable_overlap_schedule
        if (
            not self.is_draft_worker
            and (experts_capturer := get_global_experts_capturer()) is not None
        ):
            output.routed_experts_output = experts_capturer.on_forward_end(
                forward_batch=forward_batch,
                can_run_graph=output.can_run_graph,
                cuda_graph_batch=getattr(self.decode_cuda_graph_runner, "bs", None),
                no_copy_to_cpu=no_copy_to_cpu,
            )

        if (indexer_capturer := get_global_indexer_capturer()) is not None:
            output.indexer_topk_output = indexer_capturer.on_forward_end(
                forward_batch=forward_batch,
                can_run_graph=output.can_run_graph,
                cuda_graph_batch=getattr(self.decode_cuda_graph_runner, "bs", None),
                no_copy_to_cpu=no_copy_to_cpu,
            )

        if self.eplb_manager is not None:
            self.eplb_manager.on_forward_pass_end()

        if dumper.may_enable:
            dumper.step()

        if self.msprobe_debugger is not None:
            self.msprobe_debugger.stop()
            self.msprobe_debugger.step()

        if get_exec().moe.elastic_ep_backend is not None:
            self.maybe_join_ep_ranks()

        return output

    def _maybe_execute_deferred_mamba_cow_and_clear(
        self, forward_batch: ForwardBatch
    ) -> None:
        """Run deferred clear/COW on the forward stream, before the mamba layers
        read the pool, so the copies don't race the scheduler copy stream.

        No-op unless this is an extend forward on a mamba model's target worker;
        COW/clear only happen at prefix match on extend.
        """
        pool = self.req_to_token_pool
        if (
            not isinstance(pool, HybridReqToTokenPool)
            or self.is_draft_worker
            or not forward_batch.forward_mode.is_extend()
            or forward_batch.forward_mode.is_target_verify()
            or forward_batch.forward_mode.is_draft_extend_v2()
        ):
            return
        if (
            forward_batch.mamba_clear_indices is not None
            and len(forward_batch.mamba_clear_indices) > 0
        ):
            # mamba_pool is a pure PHYSICAL store; translate before zeroing or
            # clear_slots zeroes the wrong physical slots.
            pool.mamba_pool.clear_slots(
                pool.translate_mamba_indices(forward_batch.mamba_clear_indices)
            )
        if (
            forward_batch.mamba_cow_src_indices is not None
            and len(forward_batch.mamba_cow_src_indices) > 0
        ):
            if pool.mamba_ckpt_pool is not None:
                # int8 checkpoints: dequantize src int8 ckpt slot into the active bf16 dst.
                pool.mamba_ckpt_pool.load_to_active(
                    pool.mamba_pool,
                    forward_batch.mamba_cow_src_indices,
                    forward_batch.mamba_cow_dst_indices,
                )
            else:
                # mamba_pool is a pure PHYSICAL store; translate both COW slot ids.
                pool.mamba_pool.copy_from(
                    pool.translate_mamba_indices(forward_batch.mamba_cow_src_indices),
                    pool.translate_mamba_indices(forward_batch.mamba_cow_dst_indices),
                )
        forward_batch.mamba_clear_indices = None
        forward_batch.mamba_cow_src_indices = None
        forward_batch.mamba_cow_dst_indices = None

    def _forward_raw(
        self,
        forward_batch: ForwardBatch,
        pp_proxy_tensors: Optional[PPProxyTensors],
        reinit_attn_backend: bool = False,
        split_forward_count: int = 1,
    ) -> ModelRunnerOutput:
        if has_forward_context():
            ctx_mgr = contextlib.nullcontext()
        else:
            ctx_mgr = forward_context(ForwardContext(attn_backend=self.attn_backend))
        with ctx_mgr:
            mode_check = (
                forward_batch.forward_mode.is_cpu_graph
                if self.device == "cpu"
                else forward_batch.forward_mode.is_cuda_graph
            )
            can_run_graph = bool(
                mode_check()
                and self.decode_cuda_graph_runner
                and self.decode_cuda_graph_runner.can_run_graph(forward_batch)
            )

            if (
                forward_batch.forward_mode.is_decode()
                and self.hisparse_coordinator is not None
            ):
                forward_batch.hisparse_coordinator = self.hisparse_coordinator
                self.hisparse_coordinator.wait_for_pending_backup()
                self.hisparse_coordinator.num_real_reqs.fill_(forward_batch.batch_size)

            # Replay cuda graph if applicable
            if can_run_graph:
                ret = self.decode_cuda_graph_runner.execute(
                    forward_batch,
                    pp_proxy_tensors=pp_proxy_tensors,
                )
                return ModelRunnerOutput(logits_output=ret, can_run_graph=can_run_graph)

            # DP / MLP-sync padding + attn-tp normalization. Only the decode
            # cuda-graph path above pre-pads its static buffers and returns
            # early; split prefill, the prefill cuda graph, and the eager
            # forward all run the live batch and need this first — it sets
            # global_dp_buffer_len / padded token counts that graph eligibility
            # and the collectives depend on.
            self._prepare_eager_forward_batch(forward_batch)

            # Deferred mamba COW/clear on the forward stream, before the extend
            # dispatch below reads the pool.
            self._maybe_execute_deferred_mamba_cow_and_clear(forward_batch)

            dwdp_mgr = get_global_dwdp_manager()
            if dwdp_mgr is not None:
                dwdp_mgr.prefetch_first_layers()

            if forward_batch.forward_mode.is_split_prefill():
                # Layer-split mode; stays on ModelRunner, not the eager runner.
                ret = self.forward_split_prefill(
                    forward_batch,
                    reinit_attn_backend=reinit_attn_backend,
                    forward_count=split_forward_count,
                )
            elif (
                forward_batch.forward_mode.is_extend(include_draft_extend_v2=True)
                and not isinstance(self.prefill_cuda_graph_runner, EagerRunner)
                and self.prefill_cuda_graph_runner is not None
                and self.prefill_cuda_graph_runner.can_run_graph(forward_batch)
                and _prefill_cuda_graph_allows_context_parallel(
                    self.prefill_cuda_graph_runner, forward_batch
                )
            ):
                # Prefill cuda graph (piecewise).
                kwargs = self._extend_forward_kwargs(forward_batch, pp_proxy_tensors)
                category = (
                    "target_verify"
                    if forward_batch.forward_mode.is_target_verify()
                    else "extend"
                )
                # TODO: the timing here is too broad -- it also includes
                # load_batch time. Move it into the prefill cuda graph runner
                # to capture only the model.forward part.
                with device_timer_ctx(self.device_timer, category):
                    ret = self.prefill_cuda_graph_runner.execute(
                        forward_batch, **kwargs
                    )
                can_run_graph = True
            else:
                # Eager: decode / extend / idle dispatched inside the runner.
                ret = self.eager_runner.execute(
                    forward_batch, pp_proxy_tensors=pp_proxy_tensors
                )

            if (
                forward_batch.global_num_tokens_cpu is not None
                and self.pp_group.is_last_rank
            ):
                forward_batch.post_forward_mlp_sync_batch(ret)

            return ModelRunnerOutput(logits_output=ret, can_run_graph=can_run_graph)

    def _preprocess_logits(
        self, logits_output: LogitsProcessorOutput, sampling_info: SamplingBatchInfo
    ):
        # NOTE: In overlap mode, the function update_regex_vocab_mask (in sample)
        #       was executed after we processed last batch's results.

        # Calculate logits bias and apply it to next_token_logits.
        sampling_info.update_regex_vocab_mask()
        sampling_info.apply_logits_bias(logits_output.next_token_logits)

        # Release the vocab_mask GPU tensor immediately after it has been applied
        # to the logits. In overlap scheduling, the sampling_info (and its
        # grammar_mask) can be kept alive by the delay_sample_func closure and
        # batch_record_buf until the next iteration, causing a steady VRAM leak
        # when structured output (grammar) is used.
        sampling_info.grammar_mask = None

    def sample(
        self,
        logits_output: LogitsProcessorOutput,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        """Sample and compute logprobs and update logits_output.

        Args:
            logits_output: The logits output from the model forward
            forward_batch: The forward batch that generates logits_output

        Returns:
            A list of next_token_ids
        """
        self._preprocess_logits(logits_output, forward_batch.sampling_info)

        # Sample the next tokens
        next_token_ids = self.sampler(
            logits_output,
            forward_batch.sampling_info,
            forward_batch.return_logprob,
            forward_batch.top_logprobs_nums,
            forward_batch.token_ids_logprobs,
            # For prefill, we only use the position of the last token.
            (
                forward_batch.positions
                if forward_batch.forward_mode.is_decode()
                else forward_batch.seq_lens - 1
            ),
        )
        self.ngram_embedding_manager.update_after_decode(
            next_token_ids=next_token_ids,
            forward_batch=forward_batch,
        )
        return next_token_ids

    def compute_logprobs_only(
        self,
        logits_output: LogitsProcessorOutput,
        forward_batch: ForwardBatch,
    ) -> None:
        """
        Compute token_ids_logprobs without performing sampling.

        Optimized path for prefill-only requests that need token_ids_logprobs but don't
        require next token generation. Skips expensive sampling operations
        while still providing requested probability information.

        Args:
            logits_output: The logits output from the model forward
            forward_batch: The forward batch that generates logits_output
        """
        if not forward_batch.token_ids_logprobs:
            return

        # Preprocess logits (same as in sample method)
        self._preprocess_logits(logits_output, forward_batch.sampling_info)

        # Delegate to sampler for logprob-only computation
        # This populates logits_output with requested token probabilities
        self.sampler.compute_logprobs_only(
            logits_output,
            forward_batch.sampling_info,
            forward_batch.top_logprobs_nums,
            forward_batch.token_ids_logprobs,
        )

    def check_weights(self, action: str, allow_quant_error: bool = False):
        return self._weight_checker.handle(
            action=action, allow_quant_error=allow_quant_error
        )

    def _expand_eplb_metadata_for_scale(
        self,
        from_ep_size: int,
        effective_size: int,
    ) -> None:
        metadata = get_global_expert_location_metadata()
        if metadata is None:
            return
        old_num_physical = metadata.num_physical_experts
        num_local = old_num_physical // from_ep_size
        added = num_local * effective_size - old_num_physical
        if added <= 0:
            return

        initial_ep_size = get_parallel().elastic_ep_initial_size
        assert initial_ep_size is not None
        get_context().override("elastic_ep.scale", ep_size=effective_size)

        expanded_p2l = append_trivial_expert_slots(
            metadata.physical_to_logical_map,
            added,
            metadata.num_logical_experts,
            start=old_num_physical - num_local * initial_ep_size,
        )
        new_metadata = ExpertLocationMetadata.init_by_mapping(
            self.server_args,
            self.model_config,
            physical_to_logical_map=expanded_p2l,
            moe_ep_rank=self._elastic_global_rank(),
        )
        set_global_expert_location_metadata(new_metadata, allow_overwrite=True)

    def _elastic_global_rank(self) -> int:
        return self.ps.tp_rank + get_parallel().ep_join_rank_offset

    def _rearm_eplb_after_elastic_scale(self) -> None:
        if self.eplb_manager is None:
            return
        recorder = get_global_expert_distribution_recorder()
        if not recorder.recording:
            recorder.start_record()
        self.eplb_manager.enable_rebalance()

    def _reset_eplb_after_elastic_scale_failure(self) -> None:
        if self.eplb_manager is None:
            return
        set_global_expert_distribution_recorder(
            ExpertDistributionRecorder.init_new(
                self.server_args,
                get_global_expert_location_metadata(),
                rank=self._elastic_global_rank(),
            )
        )
        self._rearm_eplb_after_elastic_scale()

    def _report_elastic_scale_failure(self, error: str, effective_size: int) -> None:
        if self.ps.tp_rank != 0 or self.server_args.is_ep_scale_joiner:
            return
        from sglang.srt.managers.io_struct import ElasticScaleUpdateReq

        self._pending_elastic_scale_update = ElasticScaleUpdateReq(
            success=False,
            effective_ep_size=effective_size,
            error=error,
        )

    def _elastic_scale_ready_barrier(self, target_size: int, log_tag: str) -> None:
        if self.ps.tp_rank == 0:
            logger.debug(
                "[Elastic EP][scale] %s entering post-scale WORLD barrier "
                "(target_ep_size=%d)",
                log_tag,
                target_size,
            )
        dist.barrier(group=dist.group.WORLD)
        if self.ps.tp_rank == 0:
            logger.debug(
                "[Elastic EP][scale] %s passed post-scale WORLD barrier "
                "(target_ep_size=%d)",
                log_tag,
                target_size,
            )

    def _finalize_scale_up(
        self,
        ranks_to_join: list[int],
        target_size: int,
        effective_size: int,
    ) -> None:
        self.forward_pass_id = 0
        ElasticEPStateManager.mark_configuring_data_plane()

        state = ElasticEPStateManager.instance()
        for rank in ranks_to_join:
            state.active_ranks[rank] = 1
        state.snapshot_active_to_last()
        state.sync_active_to_cpu()
        if self.eplb_manager is not None:
            self.eplb_manager.reset_generator()

        self._expand_eplb_metadata_for_scale(
            from_ep_size=effective_size,
            effective_size=target_size,
        )
        broadcast_global_expert_location_metadata(
            model_config=self.model_config,
            moe_ep_rank=self._elastic_global_rank(),
            src_rank=0,
        )

        ElasticEPStateManager.on_scale(effective_size, target_size)
        set_global_expert_distribution_recorder(
            ExpertDistributionRecorder.init_new(
                self.server_args,
                get_global_expert_location_metadata(),
                rank=self._elastic_global_rank(),
            )
        )

        if self.eplb_manager is not None:
            self.eplb_manager.disable_rebalance(
                "EPLB rebalance is disabled while elastic EP scale-up "
                "is being finalized"
            )

        from sglang.srt.layers.dp_attention import update_dp_attention_post_scale

        update_dp_attention_post_scale(
            new_dp_size=target_size,
            new_dp_rank=self._elastic_global_rank(),
        )
        get_context().override("elastic_ep.scale", dp_size=target_size)

        ElasticEPStateManager.mark_syncing_new_world()
        self._elastic_scale_ready_barrier(
            target_size=target_size,
            log_tag="JOINER" if self.server_args.is_ep_scale_joiner else "PRIMARY",
        )
        ElasticEPStateManager.commit_scale()
        self._rearm_eplb_after_elastic_scale()

        if self.ps.tp_rank == 0 and not self.server_args.is_ep_scale_joiner:
            from sglang.srt.managers.io_struct import ElasticScaleUpdateReq

            self._pending_elastic_scale_update = ElasticScaleUpdateReq(
                success=True,
                effective_ep_size=target_size,
                slot_offset=effective_size,
                slot_count=target_size - effective_size,
            )
            logger.info(
                "[Elastic EP] Scale completed: old_ep_size=%d "
                "new_ep_size=%d joined_ranks=%s",
                effective_size,
                target_size,
                ranks_to_join,
            )

    def maybe_join_ep_ranks(self) -> None:
        if not ElasticEPStateManager.is_scaling():
            return

        state = ElasticEPStateManager.instance()
        effective_size = ElasticEPStateManager.get_effective_ep_size()
        pending_size = ElasticEPStateManager.get_pending_ep_size()

        if pending_size is None:
            if state is not None and state.has_scaled:
                error = (
                    "Elastic EP rank recovery is unsupported after runtime scale-up. "
                    "Restart the expanded deployment."
                )
                ElasticEPStateManager.fail_recovery(error)
                self._report_elastic_scale_failure(error, effective_size)
                if self.ps.tp_rank == 0 and not self.server_args.is_ep_scale_joiner:
                    logger.error("[Elastic EP] %s", error)
                return

            recovered = maybe_recover_ep_ranks(
                tp_group=self.tp_group,
                eplb_manager=self.eplb_manager,
                model_config=self.model_config,
                moe_ep_rank=self._elastic_global_rank(),
            )
            if recovered:
                self.forward_pass_id = 0
            return

        local_timeout = (
            state.pending_since is not None
            and time.monotonic() - state.pending_since
            > get_exec().moe.elastic_ep_scale_timeout
        )
        timeout = state.active_ranks.new_tensor(int(local_timeout))
        dist.all_reduce(timeout, op=dist.ReduceOp.MAX, group=dist.group.WORLD)
        if timeout.item():
            error = f"Timed out waiting for ranks to join target EP size {pending_size}"
            ElasticEPStateManager.fail_scale(error)
            self._reset_eplb_after_elastic_scale_failure()
            self._report_elastic_scale_failure(error, effective_size)
            if self.ps.tp_rank == 0 and not self.server_args.is_ep_scale_joiner:
                logger.error("[Elastic EP] %s", error)
            return

        if state.scale_phase == "waiting_for_cohort":
            cohort_target = get_scale_cohort_target(effective_size)
            if cohort_target is None:
                return
            if cohort_target != pending_size:
                error = (
                    f"Requested target EP size {pending_size} does not match "
                    f"joining cohort target {cohort_target}"
                )
                ElasticEPStateManager.fail_scale(error)
                self._reset_eplb_after_elastic_scale_failure()
                self._report_elastic_scale_failure(error, effective_size)
                if self.ps.tp_rank == 0 and not self.server_args.is_ep_scale_joiner:
                    logger.error("[Elastic EP] %s", error)
                return
            if not ElasticEPStateManager.begin_scale():
                return

        ranks_to_join = list(range(effective_size, pending_size))
        if not ranks_to_join:
            return

        current_platform.synchronize()
        ElasticEPStateManager.mark_joining()
        if try_admit_scale_ranks(ranks_to_join):
            self._finalize_scale_up(
                ranks_to_join=ranks_to_join,
                target_size=pending_size,
                effective_size=effective_size,
            )

    def _maybe_rebalance_after_rank_fault(
        self,
        output: ModelRunnerOutput,
        forward_batch: ForwardBatch,
        pp_proxy_tensors: Optional[PPProxyTensors],
        reinit_attn_backend: bool,
        split_forward_count: int,
    ) -> ModelRunnerOutput:
        if maybe_rebalance_after_rank_fault(eplb_manager=self.eplb_manager):
            output = self._forward_raw(
                forward_batch,
                pp_proxy_tensors,
                reinit_attn_backend,
                split_forward_count,
            )
        return output

    def update_model_fields(
        self,
        new_model: torch.nn.Module,
        *,
        model_path: str,
        load_format: str,
        load_config: LoadConfig,
    ) -> None:
        self.model = new_model
        get_context().override(
            "model_runner.update_model_fields",
            model_path=model_path,
            load_format=load_format,
        )
        self.load_config = load_config
